Publications

  1. G. I. Austin, I. Pe’er, and T. Korem, ‘Distributional bias compromises leave-one-out cross-validation’, Science Advances, 2025.
  2. G. I. Austin and T. Korem, ‘Compositional transformations can reasonably introduce phenotype-associated values into sparse features’, mSystems, 2025.
  3. G. I. Austin, A. Brown Kav, H. Park, J. Biermann, A.-C. Uhlemann, and T. Korem, ‘Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models’, Nature Microbiology, 2025.
  4. A. Batuure, G. I. Austin, W.A. Grobman et al., ‘A Simple Data-Driven Dietary Pattern Associated with Lower Risk for Adverse Pregnancy Outcomes’. American Journal of Obstetrics & Gynecology, 2025.
  5. G. I. Austin and T. Korem, ‘Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective’, The Journal of Infectious Diseases, p. jiae378, 2024.
  6. G. D. Sepich-Poore, D. McDonald, E. Kopylova, C. Guccione, Q. Zhu, G. Austin et al., ‘Robustness of cancer microbiome signals over a broad range of methodological variation’, Oncogene, vol. 43, no. 15, pp. 1127–1148, 2024.
  7. Q. S. Solfisburg, F. Baldini, B. Baldwin-Hunter, G. I. Austin et al., ‘The Salivary Microbiome and Predicted Metabolite Production Are Associated with Barrett’s Esophagus and High-Grade Dysplasia or Adenocarcinoma’, Cancer Epidemiology, Biomarkers & Prevention, vol. 33, no. 3, pp. 371–380, 2024.
  8. H. Kowalkowski, G. Austin, Y. Guo, L.-A. Miller-Wilson, and S. D. Byfield, ‘Patterns of colorectal cancer screening and adherence rates among an average-risk population enrolled in a national health insurance provider during 2009-2018 in the United States’, Preventive Medicine Reports, vol. 36, p. 102497, 2023.
  9. G. I. Austin et al., ‘Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data’, Nature Biotechnology, vol. 41, no. 12, pp. 1820–1828, 2023.
  10. G. Austin et al., ‘Patterns of initial colorectal cancer screenings after turning 50 years old and follow-up rates of colonoscopy after positive stool-based testing among the average-risk population’, Current Medical Research and Opinion, vol. 39, no. 1, pp. 47–61, 2023.

Preprints

  1. W. F. Kindschuh*, G. I. Austin*, Y. Meydan, et al., ‘Early prediction of preeclampsia using the first trimester vaginal microbiome’, bioRxiv, 2024.

* Equal contribution

Patent submissions

  1. E. Halperin, B. Hill, and G. Austin, ‘Methods, apparatuses and computer program products for generating predicted multi-drug contraindication data objects’. US Patent App. 18/052,508 2024.
  2. G. Austin, E. Halperin, F. Mohaghegh, and A. C. Palomera, ‘Machine learning training approach for a multitask predictive domain’, US Patent App. 18/155,228 2024.
  3. G. Austin, J. Venkataraman, F. Mohaghegh, H.R. Hassanzadeh, J.D. Stremmel, A. Saeedi, G.D. Lyng, E. Halperin, and Z.M. Poornaki, ‘Systems and methods for intelligent model training using relevant data objects’, US Patent App. 18/428,206, 2025